1. Field of the Invention
The present invention relates to a method and an apparatus for learning operation of a neural network.
2. Discussion of the Prior Art
A neural network has been known as a network which memorizes a sequence of cause and effect, and which is optimized by changing connection weights thereof through learning operation. For example, such neural network is composed of an input layer, an intermediate layer and an output layer, each of layers having plural nodes. Each of the nodes of the input layers is related to the respective nodes of the intermediate layers with respective connection weights or connection strengths, while each of the nodes of the intermediate layers is related to the respective nodes of the output layers with respective connection weights. During learning operation, dispersed plural input data are given to the neural network so as to correct or modify the connection weights so that reasonable output data are always obtained when any input data are given.
Namely, one pair of input data and teaching data are firstly used to correct the connection weights so that the output data from the neural network approach to the teaching data, which represent optimum values corresponding to the input data. This learning operation is repeated using a different pair of input and teaching data, and the teaching operation is stopped when all the pairs of input and teaching data are used for the learning operation. After the learning operation, the neural network exhibits a desirable input-output characteristic.
The above-mentioned neural network is recently used in machine tools for calculating optimum machining data based upon input and fixed condition data so as to properly carry out required machining operations.
In a neural network system used in the machine tools, each of the input data is composed of plural components or plural data items, such as material of a workpiece to be machined surface roughness, dimensional accuracy and the like, while each of the output data is composed of plural components or plural data items representing machining conditions such as rotational speed of the workpiece, infeed speed of a tool and the like.
However, all of the components of the input data and all of the components of the output data are not necessarily used all the time. There is a case in which a particular component or data item is used in a particular pair of input and teaching data, but not used in other pairs of input and teaching data.
For example, in grinding machines, several types of grinding cycles are carried out, and each of the grinding cycles uses different data items as machining data. In a pair of input and teaching data for the cylindrical grinding, data for shoulder grinding and corner grinding are omitted. In a pair of input and teaching data for shoulder grinding, data for corner grinding are omitted. Even in such event, the number of the input nodes of the neural network, namely the number of the nodes in the input layer of the neural network is set to be equal to the number of all the input data items or components of the input data, while the number of the output nodes of the neural network, namely the number of the nodes in the output layer of the neural network is set to be equal to the number of all of the output data items or components of the output data. In such system, predetermined constant values are given to the input nodes as data for omitted input data items, while some components output from the output nodes corresponding to the omitted data items are ignored, because the ignored components take meaningless values.
Such system therefore has a problem in that the connection weights of the neural network are corrected based upon the meaningless values of the ignored components, whereby the input-output characteristic of the neural network is modified in a wrong direction. This problem becomes more serious when the number of the omitted data items increases.
Further, a problem sometimes occurs in the conventional neural network system in that outputs data from the neural network take inappropriate values.
In such case, the input-output characteristic of the neural network must be corrected, and a pair of new input data and new teaching data are further added to the data base to carry out a renewal learning and thereby to correct the input-output characteristic thereof.
However, the addition of the new input data and new teaching data may sometime cause a drastic change of the input-output characteristic, whereby a part of the input-output characteristic which is to be maintained is also drastically changed.
Therefore, an operator experimentally judges whether or not the new input data and new teaching data can be used, namely, whether or not these data match with the tendency of the past learning process of the neural network. The operator then adds the new input data and new teaching data into the data base for the renewal learning of the neural network, only when the new input data and new teaching data match with the tendency of the past learning process.
As described above, in the conventional system, new input data and new teaching data must be evaluated by the operator with his experiment and sixth sense. Therefore, the renewal learning operation has been time consuming and very difficult work. Further, the neural network may sometime be renewed undesirably, whereby the input-output characteristic of the neural network is inappropriately changed to have an undesirable characteristic.